Credit points: 15

Subject outline

Quantitive analysis plays an important role in business analytics and knowledge engineering, thus it is very useful to develop computing skills for data regression and classification. This subject covers some fundamentals of computational intelligence techniques, including fuzzy inference systems, neural networks and hybrid neuro-fuzzy systems. The subject is designed with a focus on solving time-series forecasting problems using fuzzy inference systems, where fuzzy inference mechanisms and fuzzy rule extraction from numerical data are addressed. Some advanced learning techniques for training neural networks will also be highlighted. In labs and assignment students will work with business datasets for time-series prediction using a fuzzy system, which helps to consolidate the knowledge taught in the lectures and gain a hand-on experience on computational intelligence applications in business.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorJustin Wang

Available to Study Abroad StudentsYes

Subject year levelYear Level 3 - UG

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites CSE2AIF or CSE2DBF


Incompatible subjects CSE4CI AND students enrolled in any Graduate Diploma or Masters by Coursework course.

Equivalent subjects INT3CI

Special conditionsN/A

Learning resources


Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsArtificial intelligence-a guide to intelligent systems.RecommendedNegnevitsky, M.ADDISON-WESLEY, 2002.
ReadingsNeural fuzzy systems-a neuro-fuzzy synergism to intelligent systems.RecommendedLin, C.T., Lee, C.S.PRENTICE-HALL. 1996.

Graduate capabilities & intended learning outcomes

01. Describe the technologies and applications of computational intelligence systems (CIS).

Lecture 1 is on the introduction of computational intelligence systems and its applications.
Related graduate capabilities and elements:
Critical Thinking (Critical Thinking)
Inquiry/ Research (Inquiry/ Research)
Writing (Writing)

02. Describe the major components and issues in developing computational intelligence systems, such as forecasting and classification systems using fuzzy inference and neural networks.

Lectures 2, 3, 4 are on fuzzy logic and fuzzy inference systems. Lectures 5, 6, 7 are on some basics of neural networks, including neuron models, supervised and unsupervised learning algorithms. Lecture 8 is on Genetic Algorithms for solving model or parameter optimization problems.
Related graduate capabilities and elements:
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Creative Problem-solving (Creative Problem-solving)
Critical Thinking (Critical Thinking)
Inquiry/ Research (Inquiry/ Research)

03. Explain the fusion technology, i.e., hybrid intelligent systems and the links between CIS and knowledge engineering.

Lectures 9 and 10 are on hybrid intelligent system design. Lecture 11 is on knowledge engineering, where students will find the links between computational intelligence techniques and data mining and knowledge engineering for their further studies.
Related graduate capabilities and elements:
Critical Thinking (Critical Thinking)
Creative Problem-solving (Creative Problem-solving)

04. Implement a fuzzy expert system for time-series forecasting.

Lab 1 to Lab 6 are on fuzzy expert system implementation, where students will learn how to use Matlab tools to implement a computational intelligence system for resolving a time-series forecasting problem. Lab 7 and Lab 8 on neural network modeling and applications.
Related graduate capabilities and elements:
Critical Thinking (Critical Thinking)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Discipline-specific GCs (Discipline-specific GCs)
Inquiry/ Research (Inquiry/ Research)
Creative Problem-solving (Creative Problem-solving)
Ethical Awareness (Ethical Awareness)

Subject options

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Start date between: and    Key dates

Melbourne, 2016, Semester 1, Day


Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorJustin Wang

Class requirements

Lecture Week: 10 - 22
One 2.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Laboratory Class Week: 11 - 22
One 2.0 hours laboratory class per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.


Assessment elementComments% ILO*
One 3-hour examination70 01, 02, 03
One assignment report equiv. to 750 wordsHurdle requirement: In order to pass the unit, students must obtain an overall pass grade, pass the examination and pass the overall non-examination components.30 04